This administrative supplement is focused on adding targeted research questions related to Downs Syndrome (DS) across the Vanderbilt Kennedy Center?s (VKC) IDDRC, primarily enhancing Aim 3 of the Biostatistics and Bioinformatics Core, and also Aim 2 of the Clinical Translational Core. The proposed supplement focuses on Component 2 of NOT-OD-18-194 by proposing to conduct comprehensive molecular and phenotypic analysis in DS. Research activities will capitalize on Vanderbilt?s 20+ year mineable database of electronic medical records (named the Synthetic Derivative; SD) and BioVU (de-identified DNA samples that can be linked to data in the SD). SD/BioVU data allow for phenotypic and biological markers of DS to be examined, including longitudinally, in order to understand the sequela of disorders/conditions and/or biological markers associated with DS. While these are rich datasets, they are complex and require unique considerations. To this end, we have developed tools and approaches to leverage the depth of this data in order to study Autism Spectrum Disorder (ASD). In the supplement, we will apply these already developed tools and approaches to DS. Such data could be used, for example, in a predictive analytics framework to identify the best predictors of needing heart surgery. Also, we will capitalize on the BioVU DNA repository and the ongoing extensive efforts of the Vanderbilt Genetics Institute (VGI) to use a novel genetic approach to identify genetic risk factors for congenital heart disease in DS. The genotyping data developed from this will effort will also be useful for future studies of genetic risk factors for other co-morbid conditions in DS. The proposed work fits within the overarching IDDRC Aims to: (Aim 1, Overview) build an innovative research infrastructure [with] new technological or other advances to the cores and provide essential services not otherwise available to IDDRC scientists and (Aim 2, Overview) to improve the health, education, and well-being of people with autism, learning disabilities, acquired disabilities, and genetic syndromes. More explicitly, though, it directly enhances the Biostatistics and Bioinformatics Core?s Aim 3, which focuses on developing electronic medical record (EMR) and genomic databases in a range of IDD conditions, including identifying specific variables of interest in the EMR; designing and programming optimal algorithms for identifying patients of interest from the SD; and, linking these data to existing patient DNA sequence or genotype data. Aim 3 of the Biostatistics and Bioinformatics Core crosses with the Clinical and Translational Core?s Aim 2 goal of facilitating recruitment and data mining of longitudinal social, cognitive, and medical phenotypes in IDDs. Thus, the proposed activities enhance existing IDDRC aims, but rest squarely within the scope of them. The proposed work is targeted to be completed within a one-year timeframe, and the expected outcomes are to: (1) determine phenotypic associations with DS; (2) determine longitudinal predictors of outcomes; (3) determine how genetic variants within DS are related to these outcomes; and (4) increase VKC DS Registry and DSConnect registrants.